Improving Neural Indoor Surface Reconstruction with Mask-Guided Adaptive Consistency Constraints

3D scene reconstruction from 2D images has been a long-standing task. Instead of estimating per-frame depth maps and fusing them in 3D, recent researches leverage the neural implicit surface as a global representation for 3D reconstruction. Equipped with data-driven pre-trained geometric cues, these methods have demonstrated promising performance. However, the inevitable inaccurate estimation of priors can lead to suboptimal reconstruction quality, particularly in some geometrically complex regions. In this paper, we propose a two-stage training process to further improve the reconstruction quality. It decouples the view-dependent and view-independent colors, and leverages two novel consistency constraints to enhance detail reconstruction performance without requiring extra priors. Additionally, we introduce an essential mask scheme to adaptively influence the selection of supervision constraints, thereby improving performance in a self-supervised paradigm. Experiments on synthetic and real-world datasets show the capability of reducing the side effects of inaccurately estimated priors and achieving high-quality scene reconstruction with rich geometric details.